{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Decision Tree Classifier with Grid Search\n",
    "Decision tree is trained on feature set 2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Get the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Stored variables and their in-db values:\n",
      "X_16_val                  -> array([[ 3.00880939,  0.26443017,  1.05370334, ...\n",
      "X_32_val                  -> array([[-0.13964146,  0.53184264, -0.71694033, ...\n",
      "X_32test_std              -> defaultdict(<class 'list'>, {0: array([[-0.1396414\n",
      "X_32train_std             -> array([[-0.80277066, -0.49489511, -0.83240094, ...\n",
      "X_test                    -> defaultdict(<class 'list'>, {0: array([[[-0.006215\n",
      "X_test_std                -> defaultdict(<class 'list'>, {0: array([[ 3.0088093\n",
      "X_train                   -> array([[[-0.01174874, -0.00817356, -0.0042913 , ..\n",
      "X_train_std               -> array([[-0.80277066, -0.49489511, -0.83240094, ...\n",
      "snrs                      -> [-20, -18, -16, -14, -12, -10, -8, -6, -4, -2, 0, \n",
      "y_16_val                  -> array([3, 5, 1, ..., 2, 4, 0])\n",
      "y_32_test                 -> defaultdict(<class 'list'>, {0: array([2, 2, 3, ..\n",
      "y_32_train                -> array([5, 0, 2, ..., 4, 6, 6])\n",
      "y_32_val                  -> array([2, 2, 3, ..., 0, 3, 5])\n",
      "y_test                    -> defaultdict(<class 'list'>, {0: array([3, 5, 1, ..\n",
      "y_train                   -> array([5, 0, 2, ..., 4, 6, 6])\n"
     ]
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "\n",
    "import numpy as np\n",
    "from collections import defaultdict\n",
    "from time import time\n",
    "import pickle  \n",
    "import sklearn\n",
    "from sklearn import metrics\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "\n",
    "%store -r\n",
    "%store"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training data:  (80000, 32) and labels:  (80000,)\n",
      " \n",
      "Test data:\n",
      "Total 20 (4000, 32) arrays for SNR values:\n",
      "dict_keys([0, -16, 2, 4, 6, 8, 12, 10, -20, -14, -18, 16, 18, -12, 14, -10, -8, -6, -4, -2])\n"
     ]
    }
   ],
   "source": [
    "print(\"Training data: \", X_32train_std.shape, \"and labels: \", y_32_train.shape)\n",
    "print(\" \")\n",
    "print(\"Test data:\")\n",
    "print(\"Total\", len(X_32test_std), X_32test_std[18].shape, \"arrays for SNR values:\")\n",
    "print(X_32test_std.keys())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Train and test the classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Grid search took 1178.00 minutes \n",
      "   \n",
      "Result of grid search:\n",
      "DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=12,\n",
      "            max_features=None, max_leaf_nodes=149,\n",
      "            min_impurity_split=1e-07, min_samples_leaf=1,\n",
      "            min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
      "            presort=False, random_state=42, splitter='best')\n"
     ]
    }
   ],
   "source": [
    "#Train the classifier\n",
    "\n",
    "params = {'max_depth': list(range(9,18)),'max_leaf_nodes': list(range(50, 150)), 'min_samples_split': [2,3,4],\n",
    "          'max_features': [None,'auto','sqrt','log2'], 'criterion':['gini', 'entropy']}\n",
    "\n",
    "grid_search_cv = GridSearchCV(DecisionTreeClassifier(random_state=42), params, verbose=0)\n",
    "\n",
    "start = time()\n",
    "grid_search_cv.fit(X_32train_std, y_32_train)\n",
    "print(\"Grid search took %.2f minutes \"%((time() - start)//60))\n",
    "\n",
    "print(\"   \")\n",
    "\n",
    "print(\"Result of grid search:\")\n",
    "print(grid_search_cv.best_estimator_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test the classifier\n",
      " \n",
      "Decision Tree's accuracy on -20 dB SNR samples =  0.12075\n",
      "Decision Tree's accuracy on -18 dB SNR samples =  0.125\n",
      "Decision Tree's accuracy on -16 dB SNR samples =  0.132\n",
      "Decision Tree's accuracy on -14 dB SNR samples =  0.13125\n",
      "Decision Tree's accuracy on -12 dB SNR samples =  0.14725\n",
      "Decision Tree's accuracy on -10 dB SNR samples =  0.17625\n",
      "Decision Tree's accuracy on -8 dB SNR samples =  0.24925\n",
      "Decision Tree's accuracy on -6 dB SNR samples =  0.34075\n",
      "Decision Tree's accuracy on -4 dB SNR samples =  0.417\n",
      "Decision Tree's accuracy on -2 dB SNR samples =  0.43425\n",
      "Decision Tree's accuracy on 0 dB SNR samples =  0.5135\n",
      "Decision Tree's accuracy on 2 dB SNR samples =  0.6345\n",
      "Decision Tree's accuracy on 4 dB SNR samples =  0.7895\n",
      "Decision Tree's accuracy on 6 dB SNR samples =  0.823\n",
      "Decision Tree's accuracy on 8 dB SNR samples =  0.83325\n",
      "Decision Tree's accuracy on 10 dB SNR samples =  0.83025\n",
      "Decision Tree's accuracy on 12 dB SNR samples =  0.83725\n",
      "Decision Tree's accuracy on 14 dB SNR samples =  0.83425\n",
      "Decision Tree's accuracy on 16 dB SNR samples =  0.826\n",
      "Decision Tree's accuracy on 18 dB SNR samples =  0.8255\n"
     ]
    }
   ],
   "source": [
    "#Test the classifier\n",
    "\n",
    "import collections\n",
    "\n",
    "y_pred = defaultdict(list)\n",
    "accuracy = defaultdict(list)\n",
    "\n",
    "print(\"Test the classifier\")\n",
    "print(\" \")\n",
    "for snr in snrs:\n",
    "    y_pred[snr] = grid_search_cv.predict(X_32test_std[snr])\n",
    "    accuracy[snr] = metrics.accuracy_score(y_32_test[snr], y_pred[snr])\n",
    "    print(\"Decision Tree's accuracy on %d dB SNR samples = \"%(snr), accuracy[snr])   \n",
    "    \n",
    "accuracy = collections.OrderedDict(sorted(accuracy.items()))  #sort by ascending SNR value"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##  Visualize classifier performance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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ffc4fLbDLuTtXXHadvlSMD+beQ79P/0HYmNt4fUIaBs+8g/9+m44ZK+5jQVw2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A8ePFB4Hz6fj3/++Qcff/wxTExM5D7nzJkz8Pb2xtChQzF+/Hjs2bMHQqFQ6fb17NkTAFBQUFDl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\nmQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a6dbb840f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.style.use('classic')\n",
    "\n",
    "fig = plt.figure(figsize=(8, 4), dpi=100)\n",
    "x = snrs\n",
    "y = list(accuracy.values())\n",
    "plt.plot(x, y, marker=\"o\", linewidth=2.0, linestyle='dashed', color='royalblue')\n",
    "plt.axis([-20, 20, 0, 1])\n",
    "plt.xticks(np.arange(min(x), max(x)+1, 2.0))\n",
    "plt.yticks(np.arange(0, 1, 0.10))\n",
    "\n",
    "ttl = plt.title('SNR vs Accuracy', fontsize=16)\n",
    "ttl.set_weight('bold')\n",
    "plt.xlabel('SNR (dB)', fontsize=14)\n",
    "plt.ylabel('Test accuracy', fontsize=14)\n",
    "plt.grid()\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Confusion Matrix Without Normalization\n",
      "       8PSK  BPSK  CPFSK  GFSK  PAM4  QAM16  QAM64  QPSK\n",
      "8PSK    446     4      0     0     3     50     52     8\n",
      "BPSK      0   480      0     0     9      3      5     2\n",
      "CPFSK     5     0    497     2     0      9      9     0\n",
      "GFSK     16     1      3   498     1      7     11     3\n",
      "PAM4      1    14      0     0   486      4      5     2\n",
      "QAM16     4     0      0     0     0    197    189     5\n",
      "QAM64     8     1      0     0     1    204    222     4\n",
      "QPSK     20     0      0     0     0     26      7   476\n"
     ]
    }
   ],
   "source": [
    "# Confusion Matrix\n",
    "\n",
    "from sklearn.metrics import confusion_matrix\n",
    "import pandas as pd\n",
    "\n",
    "classes = ['8PSK', 'BPSK', 'CPFSK', 'GFSK', 'PAM4', 'QAM16', 'QAM64', 'QPSK']\n",
    "y_predicted = grid_search_cv.predict(X_32test_std[18])\n",
    "conf_matrix = confusion_matrix(y_predicted, y_32_test[18]) \n",
    "\n",
    "df = pd.DataFrame(data = conf_matrix, columns = classes, index = classes) \n",
    "print(\"Confusion Matrix Without Normalization\")\n",
    "print(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Confusion Matrix\n",
      "       8PSK  BPSK  CPFSK  GFSK  PAM4  QAM16  QAM64  QPSK\n",
      "8PSK   0.79  0.01   0.00  0.00  0.01   0.09   0.09  0.01\n",
      "BPSK   0.00  0.96   0.00  0.00  0.02   0.01   0.01  0.00\n",
      "CPFSK  0.01  0.00   0.95  0.00  0.00   0.02   0.02  0.00\n",
      "GFSK   0.03  0.00   0.01  0.92  0.00   0.01   0.02  0.01\n",
      "PAM4   0.00  0.03   0.00  0.00  0.95   0.01   0.01  0.00\n",
      "QAM16  0.01  0.00   0.00  0.00  0.00   0.50   0.48  0.01\n",
      "QAM64  0.02  0.00   0.00  0.00  0.00   0.46   0.50  0.01\n",
      "QPSK   0.04  0.00   0.00  0.00  0.00   0.05   0.01  0.90\n"
     ]
    },
    {
     "data": {
      "image/png": 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zHkBU+UyxSftPOzPQ5rrsdb6k1UjrzPUH3putGHBpPStoZmbNr7e0FDvzTHEr\noNAiHEmaE/JB4MukZ41mZtbnqKr/mnWkTWeC4qrAnOzPewB/jIglpJUE1q1TvXKRNFTSLyT9N9uA\n8iVJ/5Z0dGFV9GyjydaS17NFZWwu6VpJs7IyZkr6ffbcFEnrZOdsXnTOqpJukfRw1n1sZtanddHk\n/W7Xme7TJ4HPSPo/0lyQX2XpQ+jGgTaS1iMt9fM68H3SPMmFpMUFjiIt6fMXIIAfApcUnb40K2MI\naVuSP5MC/JukwL4/sApp7T2yMgrXfR9wPbAY2KFozy4zsz6rt3SfdiYongZMAM4HbouI27P03YD7\n61WxHH4NLAK2iogFRelPk555Fns7Il4pU8b2wGrAkRFR2N/pGd7tHi4QgKS1gRuB54ADImJeTXdg\nZtZLdNXk/e5WdfdpRPwe+Ahpz6rdig7dARxXp3q1S9IapIUDzisJiNV6mfSLwec7yBfAxsBtpBbp\nPg6IZma9T2eeKRIRz0bEndmzxELabRHxcP2q1q4NSK23x4sTJb0q6a3sdUbRoTOL0udK+kZW57uB\n04ErJc2W9DdJx2cLnbcpmtQ6/i9wYLZ6u5mZZbpi7dNG6NR+itmgk5HAh4GBxcci4uA61KuztiYF\n+qtIa7QW/IxsVfVM4VkhEXGSpHOAXYBtgaOB/5X0qYh4pOica0k7Of8PcE3eCn33hOMZPHi1NmkH\nfmEUo0a15C3CzPqYiRMnMmnSxDZpc+bOqZC7SfSSh4qdmbz/eWAi6bnbjtnPDYHVgb/VtXaVPUHq\n0tyoODEins7qOL8k/+yIeKpSYRHxBvAH4A+S/pf0bPR4oLDYbJCepT4EXCVJEXF1nor+7KxxDB8+\nPE9WMzMAWlpaaGlp+4vztPumse222zSoRjlUO6K0OWNip7pPfwScEBG7kwa6HE0Kin8CHmnvxHqJ\niNeBvwPfkLRyncteQhphu0pRsrJjPwFOBn4n6cB6XtfMrCfrs2ufkgJgYbuoRcAqEbFE0lmkQHVa\nvSrXga+TBr7cK+kU4EHS7srbkAbFdLg4uaR9gBZSy/dxUvDbH9ibtBjBciLidElLSc8h+0XExHL5\nzMz6kl7Se9qpoPgG77aiXgQ2IXUrrgq8p0716lBEPCVpOGnXjtOBD5HmKT5KeoZY2H8rypcAWd53\ngHGkjSoXkgbTHBERVxVfruTaZ0pqBSZIwoHRzPq6vrz26e2kQSkPA/8H/ELSp4C9gFvrV7WORcQs\n0o7KFXdGutIOAAAgAElEQVRVjoj12zk2k9T92941niGt7Vqa/jNS8DUz6/P6ckvxm0DhOd6ppC7L\nT5ImtY+tU73MzKwHqXY10yaNiZ3aT/GVoj8vIQ08MTOzvqzKFW3yNhUlHUOaDTAMeAD4ZkSUHTMi\naSfglpLkAD5QYVWz5eQKipIGdpwru3rEorx5zcysd+iK7lNJo4CzSetZ3wOMAaZI+mhEzK5wWgAf\nBd5alpAzIEL+luIC2h+wUmy5529mZta7ddHap2OAiyJiQnbO0cA+wOHAWe2c92pEzM1dmSJ5g+Le\nnSnczMz6hnq3FCUNIO3fe3ohLSJC0k3Adu2dCtyfbR/4MHByRNyRt165gmJETMlboJmZWR0MIfU8\nzipJn0XJamZFXgK+CtxLWurzSOBWSdtERK5dnDqzzNshwLyI+L+S9M8DK2a7aJiZWR/S3jzFe+69\nkf/ce1ObtPnz67/9bkQ8TtuNIu6S9BFSN+xhecrozJSMkyg/t+8N0oR5B0Uzs76mne7Tbbfeg223\n3qNN2jPPPsZpPx1d/oRkNmlD+KEl6UNJ2/7ldQ9p79xcOrP26brAzDLpM4F1OlGemZn1cFVtG5Xj\n+WO2Rd9UYNd3ryFl73M/IwS2JHWr5tKZluKrwKakHeqLbQq82YnyzMysh+ui0afnAJdLmsq7UzIG\nkW0FmO2bu1ZEHJa9/zapgfYIsBLpmeKnSZvS59KZoDgZ+KWk1yPizqwinwR+kR0zM7M+pivmKUbE\nZElDSKunDSVt67dnRLyaZRlGWre6YCBpXuNawDzSRhG7RsS/8tarM0HxRNLO97cX7Vu4EjAJ+EEn\nyuvV+vfvxworNH7q5pIlSxtdBYCm+CwKrn/jxEZXYZndVjyl0VVY5u8LftToKgDQr1/zLARW1Uot\nXVWHpl0YLRHVfU55c0bEBby7wUPpsdEl72tek7ozy7wtAD4raTNSX+184MFs1I+ZmfVFvWTx0860\nFAGIiIdIW0aZmVlf10Vrn3a3TgdFMzOzgi4aaNPtHBTNzKxmfXk/RTMzszbaW9GmUv5m5KBoZmY1\n6y0txc6saIOkbSRdIukWSWtlaS2SPlHf6pmZmXWfqoOipP2Bf5JWIN+ONEcR4P3AD+tXNTMz6zGy\ngTZ5X83aVOxMS3Es8I2I+BKwuCj9NtLeV2Zm1sekOFdNYGx0jcvrzDPFjYGby6S/CaxeW3XMzKwn\n6svPFF8B1iuTvh3ld88wM7NerjD6NPerF40+HQ+cK+lQIIA1JQ0HxgFn1bNyZmbWM6ifUBXr1VaT\ntzt1pqX4E+DPwJ3AqsBdwFXA7yLi53WsW1mSxktqlbRU0kJJ/5V0kqR+JflmSJov6f1lyrg1K+OE\nMsf+mh0ruyqypAuz49+q312ZmfVw1e6l2JwxsfqgGBGtEXES8D7g46S9qoZFxHfrXbl2XE/aMmQD\n0oroY4HjCwclbU8aHXsN8OUy5wfwbOmxbHrJLsCL5S4q6XPAtsALNdbfzKxXqW6QTZXrpHajTs1T\nBIiIdyJiWkT8KyLeqGelclgYEa9GxHMRcTFwE/DZouNHkLVegcMrlPEXYIik7YrSDgOmkJ6btiHp\ng6Q9Iw8GltR+C2ZmvUfaOqqKV6MrXEHVzxQl/a294xHxmc5Xp9MWAGsCSHoP8AVga+BxYLCk7SPi\n9pJzFgFXkoLmnVnal4HvAm02t1P6lWYCcFZETG/W33DMzBqltywI3pmW4jMlrxdJE/c/mb3vVpJ2\nA/bk3WkiLcDjETEjIlqB35NajuWMBw6UtLKkHYHVSC3IUt8HFkXEefWtvZlZ79Bn5ylGxNfKpUs6\nne5rEe8n6S1gQHbNK3m3dTea1G1acBVwq6RvRsQ7xYVExIOSHie1LD8NTIiI1uLfYCRtBXwLGN6Z\nih53/LEMHjy4TVrLqBZaWg7qTHFm1gdMnPh7Jk6a2CZtzpw5DapNTtUuUtNbgmI7xpO6IX9QxzIr\n+QdwNGlFnRezFiGSNgE+AWwtqXh6SD9SC/LSMmWNB44BNiF1uZbagTSo6LmiYNkfOEfSdyJi/fYq\neva4cxgxYkTe+zIzo6XloOV+cZ42bRrbbFvuK6pJdNHsfUnHkAZSDgMeAL4ZEf/Jcd72wK3AQxGR\n+0u40wNtyhhB22XfutI7ETEzIp4vBMTMEaR1WTcHtih6/ZzKXahXAZuRPrjHyhyfUKa8F0lzMves\nw72YmVkZkkYBZ5NmGAwnBcUpkoZ0cN5g4ArSIMyqdGagzVWlScAHgO1p4OR9SSsAXwJ+GBHTS45d\nAhwraZPSYxHxpqRhVAjo2cjaNqNrJS0GXo6I/9bzHszMeqou2k9xDHBRREwAkHQ0sA9pgGR78eZC\n0mO1VtrOTOhQZ1qKKnm1AvcD/xMRJ3aivHrZH1gD+FPpgYiYATxKhdZiRMyNiPnFSR1cq6PjZmZ9\nSlXTMXL0tEoaQNpkYtla2xERpNbfdu2cN5q0FOkplfK0p6qWoqT+pK7IxyKiIU99I2J0hfQ/kgbe\nVDpv06I/f7qDa7Tb/9zRc0Qzs76mC5Z5G0IavzGrJH0WsFHZMqUNgdOBHUoHTeZVVVCMiKWS/k0a\nlNLkQ6HMzKy7tNf6++c//8q//912ivs777xV5+urH6nLdGxEPFlIrraczow+fRRYG3iqE+eamVmv\nVPmZ4s4778vOO+/bJu2JJx5lzJiR7RU4G1gKDC1JHwq8XCb/e0hLj24p6fwsrR9p/ZVFwB4RcWsH\nN9GpZ4onAOMk7SZpdUkDi1+dKM/MzHq4ek/ej4jFwFRg13evIWXv7yhzylxgU2BL3p0pcCEwI/vz\n3XnuozMtxSklP0v170SZZmbWg3XRNMVzgMslTQXuIY1GHQRcnsrQGcBaEXFYNgjn0bbX0CvAgtJZ\nB+3pTFDcuxPnmJlZL9YVa59GxORsTuKppG7T+4E9I+LVLMsw0uO8uskdFLP9BcdFRKUWopmZ9VFd\ntSB4RFwAXFDhWNnZCEXHT6HKqRnVPFMcS9pU2MzMbDn1mqPYSNV0nzbxbZiZWSP1lq2jqn2m6JVc\nzMxsOX01KD4uqd3AGBFr1FAfMzOzhqk2KI7FK9mYmVmJLpqS0e2qDYoTI+KVLqlJLxUEafpMY62w\ngqePNrObFo5tdBWWueSiuxpdBQAGDKjnzna1OXR04/cxjCZ/eiXlWs+0Tf5mVE1QbO6/ETMza5xq\nR5X2gqDYpLdgZmaNpuy/avI3o9xBMSKapy/DzMyaS2GH3WryN6HOLPNmZmbWRl+dkmFmZracvjr6\n1MzMbDlqZz/FSvmbkYOimZnVrg+OPjUzMyvLzxTNzMwyfqZoZmaWSUGx569o47mHZmZmGbcUzcys\nZr2l+7RpWoqSPiTpMkkvSFoo6WlJ50pabisqSQdJWiLpV2WO7SSpVdJrkgaWHPt4dmxpUdqKksZL\nelDSYkl/rFC/gZJOy+q1QNJTkr5ch1s3M+vxxLuBMdcrb7nSMZJmSpov6S5JFVdnl7S9pNskzZY0\nT9J0Sd+p5j6aIihKWg+4F/gIMCr7+VVgV+BOSe8tOeVw4EzgoNLAV+Qt4HMlaUcAz5Sk9QfmAb8A\n/t5ONa8GPg2MBj4KHAQ81k5+M7M+RFX9lycsShoFnE3atnA48AAwRdKQCqe8A/wK+BSwMfBj4CeS\nvpL3LpoiKAIXAAuB3SPitoh4PiKmALsBHwROK2TMAuh2wE+B/wKfr1DmFaQgWDhvJaAlS18mIuZF\nxDERcSkwq1xBkvYifcifiYhbIuLZiLg7Iu7s3O2amfUuVbUS83e1jgEuiogJETEDOJrUiDm8XOaI\nuD8iJkXE9Ox7+ipgCun7O5eGB0VJqwN7AOdHxKLiYxExC7iS1Hos+DLw14h4C/gdUO43gAB+C3xK\n0oeytJHATOC+TlRzP1JL9nuSnpf0mKSfZYHWzKzPK8xTrObVQXkDgK2AmwtpkTanvYnUMMpTp+FZ\n3lvz3kfDgyKwIakdPaPC8enA6pKGKH2KXyYFPICJwPaS1ilz3ivA9Vl+SN2el3WyjuuTftP4f8AB\nwLdJQfb8TpZnZtardEFLcQjp8VZpD94sYFj7ddFzkhYA95AaXOPz3kczjT7t6CNaRGpRDiIFOyLi\nNUk3kZrS5bYuvww4V9KVwCdIgWzHTtStH9AKHBwRbwNIOha4WtLXI2JhpROPP/44Bq82uE3aqFEt\ntLS0dKIaZtYXTJw4kUmTJrZJmzN3ToNqk097rb8bbvgTU6Zc2ybt7bfndmV1dgBWJX3vnynpiYiY\nlOfEZgiKT5C6OzcBri1z/GPAqxExV9IRwBrAgqIPX8BmlA+K1wMXA5cC10XEG51cWugl4IVCQMxM\nz679IeDJSieOG3c2I4aP6Mw1zayPamlZ/hfnafdNY9ttt2lQjfKp9PW6994HsPfeB7RJmz79Ib74\nxb3bK242sBQYWpI+FHi5vRMjojCg8hFJw4CTgVxBseHdpxHxOmnU59clrVh8LLuZg4Hx2dSM/UnP\nF7coeg0nda/uUabspcAEYCdSYOys24G1JA0qStuI1Hp8voZyzcx6hXo/U4yIxcBU0iyEwjWUvb+j\niqr1B1bsMFem4UEx8w1SpadI+lQ2Z3Ev4EbSs8YfA4cCsyPimoh4tOj1EKlFWDzgpvjT/iHwvoio\nON1C0iaStiS1QgdL2kLSFkVZrgJeIwXnTSTtCJwFXNpe16mZWZ+hTrw6dg5wpKRDJW0MXEh6hHY5\ngKQzJC2bUSDp65L2lbRB9joCOI53x6F0qBm6T4mIJ7IJmSeTmrjvJwXsPwBfiogFkkYDZSfWZ/km\nFE30j6KylwCvd1CFvwEfLnp/X1ZG/6yMdyTtTpr/8h9SgJwEnJT3Hs3MerOuWPs0IiZncxJPJXWb\n3g/sGRGvZlmGAWsXndIPOANYF1hCerT13Yi4OG+9miIoAkTEsxTNPZE0FjgW2By4JyK2aOfcq0mT\n6wH+SRbMKuS9tvR4RKyXo36PA3t2lM/MrC/qqmXeIuIC0lz2csdGl7w/Dzgvfy2W1zRBsVREnCLp\nadLooXsaXB0zM+sDmjYoAkTEFR3nMjOzRhNVbjKce/XT7tXUQdHMzHqO5gxz1XFQNDOzmuWZZlGa\nvxk5KJqZWc16y36KDopmZlYztxTNzMwybimamZkVadZAVw0HRTMzq5m7T83MzDLuPjUzM8t0xdqn\njeCg2MWiNWhtjY4zdrH+/Zv0X6A1nVVWHdjoKgDwztuLGl0F64McFM3MrGa95Zlis+ynaGZm1nBu\nKZqZWV00aeOvKm4pmpmZZdxSNDOzmvWWKRluKZqZWc3Uif9ylSsdI2mmpPmS7pK0dTt5PyfpRkmv\nSJoj6Q5Je1RzHw6KZmZWO3Xi1VGR0ijgbGAsMBx4AJgiaUiFU3YEbgT2BkYAtwDXSdoi7204KJqZ\nWc0K3afVvHIYA1wUERMiYgZwNDAPOLxc5ogYExHjImJqRDwZEScC/wX2y3sfDopmZlazenefShoA\nbAXcXEiLiABuArbLVac0GfI9wOt578NB0czMalf/7tMhQH9gVkn6LGBYzlp9F1gFmJwzv0efmplZ\nfVSKc9de+wf+fN0f2qTNnTu3a+siHQycBOwfEbPznuegaGZmNROVl3k74ICRHHDAyDZpDz30APvs\nu3N7Rc4GlgJDS9KHAi+3WxepBbgYGBkRt7Rb8RJN030q6UOSLpP0gqSFkp6WdK6kNcrkPUjSEkm/\nKnNsJ0mtkl6TNLDk2MezY0vLnHe8pMckLZD0nKQfVKjn9pIWS5pWy/2amfUqde4+jYjFwFRg12WX\nSFF3V+COitWQDgIuBVoi4oZqb6MpgqKk9YB7gY8Ao7KfXyXd/J2S3ltyyuHAmcBBpYGvyFvA50rS\njgCeKXP9X2ZlHgtsBOwP3FMm32DgCtKDXjMzy3TBjAyAc4AjJR0qaWPgQmAQcDmApDMkXbGsDqnL\n9ArgOOA/koZmr9Xy3kdTBEXgAmAhsHtE3BYRz0fEFGA34IPAaYWMWQDdDvgpaajt5yuUeQUpCBbO\nWwloydIpSt+ENMx3/4j4a0Q8ExH3RcTNLO9C4Ergrs7dppmZ5RURk4HjgVOB+4DNgT0j4tUsyzBg\n7aJTjiQNzjkfeLHodW7eazY8KEpaHdgDOD8i2mygFhGzSEFoVFHyl4G/RsRbwO+Ar5QpNoDfAp+S\n9KEsbSQwk/TBFtsXeBLYX9JT2coJv8nqVVzP0cB6wCnV36WZWe9W2GQ4/ytfuRFxQUSsGxErR8R2\nEXFv0bHREbFL0ftPR0T/Mq+y8xrLaYaBNhuSWtIzKhyfDqyerWDwGikoHpMdmwiMk7RORJR2i74C\nXJ/l/wkwGrisTPnrA+uSguYXSZ/JucDVpJYqkjYETgd2iIjWavYBO/67xzN48OA2aaMOHMWoUS25\nyzCzvmXixIlMmjSxTdqcuXMaVJu+pRmCYkFHkWYRqUU5iBTsiIjXJN1Eeh44tsw5lwHnSroS+AQp\n8O1YkqcfMBD4UkQ8CSDpCGBqFgyfJLVWxxaO56jrMuN+No7hw0fkzW5mRktLCy0tbX9xnnbfNLbd\ndpsG1ahjXhC8fp4gdXduUuH4x4BXI2Iu6RnhGsCCbAToYtIad4dVOPd6UhC9FLguIt4ok+clYElR\nwIPUOgX4MGk1hI8D5xVd8yRgS0mLJO2c8z7NzHqvqrpOq4yg3ajhQTEiXgf+Dnxd0orFxyQNAw4G\nxmdTM/YnPV/coug1nNS9utxK6BGxFJgA7EQKjOXcDqyQDeAp2IgUqJ8B5gKbAlsWXfNCUnfvFsDd\n1d+1mZk1o2bpPv0GKThNkXQSaUDMpsBZpODzY+AoYHZEXFN6sqTrSQNubiwkFR3+IXBWFnzLuQmY\nBlwmaQxp5NJ5wI0R8USW59GS670CLIiI6ZiZWZpmUU33aZfVpDYNbykCZMFna+ApYBLwNPA34DHS\n4JZ5pIEyf6xQxB+A/Yom+kdR2UvaCYiFBWb3I62e8E/gOuAR4KAabsnMrE/pqv0Uu1uztBSJiGcp\n2g5E0ljSZPrNgXsiouJ+WBFxNWm0KKTA1r+dvNeWHo+Il4EvVFHXU/DUDDOzd1UxI39Z/ibUNEGx\nVEScIulp0qjR5VaXMTOz5tFbRp82bVAEiIgrOs5lZmaN1ksais0dFM3MrIfoJU1FB0UzM6uL5gxz\n1WmK0admZmbNwC1FMzOrWS/pPXVQNDOzeqh26bbmjIoOimZmVjOPPjUzM8u4+9TMzKyNJo10VXBQ\n7GKtrdDaGh1n7GL9Ky58Z9bW/3xh80ZXAYABA5rnH+1+w85sdBWYs+SFRlehXb2lpegpGWZm1rQk\nHSNppqT5ku6StHU7eYdJulLSY5KWSjqn2us5KJqZWe30bmsxzytPT6ukUcDZwFjS3rkPkLYYHFLh\nlBWBV0jbDd7fmdtwUDQzszpQJ14dGgNcFBETImIGcDQwj6IdlYpFxDMRMSYifkfaIL5qDopmZlaz\nalqJeZ4/ShoAbAXcXEjL9r+9Cdiuq+7DQdHMzJrRENLet7NK0mcBw7rqoh59amZm9VGh9XfNNZO5\n5prJbdLmzJ3TDRWqnoOimZl1qZEjD2TkyAPbpN1//33stPP27Z02G1gKDC1JHwq8XNcKFnH3qZmZ\n1Uyd+K89EbEYmArsuuwakrL3d3TVfbilaGZmzeoc4HJJU4F7SKNRBwGXA0g6A1grIg4rnCBpC1JH\n7qrA+7L3iyJiep4LOiiamVnNumJFm4iYnM1JPJXUbXo/sGdEvJplGQasXXLafUBhGbERwMHAM8D6\neerloGhmZk0rIi4ALqhwbHSZtJoeCzoomplZ7XrJ4qc9eqCNpA9JukzSC5IWSnpa0rmS1ijKc6uk\n1uw1X9Ijkr5WdLyfpO9Lmi5pnqTXsvX1Di/KM17SH0uuPTIrb0z33K2ZWfPqkvVsGqDHBkVJ6wH3\nAh8BRmU/v0oamXSnpPdmWQO4mNQfvQkwGThfUmF88MnAt4ETs+M7AxcBhfPLXfsrwG+Br0bEz+t5\nX2ZmPVIviYo9ufv0AmAhsHtELMrSnpd0P/AkcBpwTJY+L3sw+ypwiqSDgM+SAuR+wAURUdwSfKjS\nRSWdQFqcdlRE/LmeN2Rm1pM1aZyrSo9sKUpaHdgDOL8oIAIQEbOAK0mtx0oWAAOzP78M7NLOquvF\n1/0pqUW5jwOimVmRei9+2iA9MigCG5J+KZlR4fh0YPXSQJc9P/wisBnvLjJ7LPA+4GVJD0j6taS9\nypT5GeC7wGcj4tY63IOZmTWZntx9Ch231gutyGMkHUlqHS4BzomICwGyCZ2bStoK2B7YEbhO0viI\nOKqorAdIC9SeKmnviHgnTwVPOOF4Bg9erU3aFw4cxagDW/KcbmZ90IsLH+ClRQ+2SVsSCxpUm3yq\nfUzYnO3EnhsUnyANoNkEuLbM8Y8Br0bE3LQqEL8jPWOcHxEvlSswIqaSlhT6paRDgAmSTouIZ7Is\nLwAjgVuBGyTtlScwnnXWOIYPH17VzZlZ37bWiluw1opbtEmbs+QF7pxbdrpe82jWSFeFHtl9GhGv\nA38Hvi5pxeJjkoaRVjAYX5Q8JyKeqhQQyygsB7RKyXWfA3YiraIwRdIqpSeamfVF9V77tFF6ZFDM\nfANYkRScPpXNWdwLuJH0rPHHeQqRdLWk70jaRtKHJe0MnAc8RplnlhHxPCkwvh+4UdJ76nM7ZmbW\naD02KEbEE8DWwFPAJOBp4G+kYLZDRMwrZO2gqBuAfYE/Z+eOBx4lra/XWuHaL5IC45qkrtRVa7oZ\nM7OezvMUGy8ingWKV54ZSxpNujlpRXUiYpcOyrgUuLSDPOXW13sJ2Lj6WpuZ9T4eaNOEIuIUSU8D\nnyALimZm1g16SVTsVUERICKuaHQdzMz6piaNdFXodUHRzMy6Xy9pKDoomplZHfSSqOigaGZmNesl\nMbHnTsnoKyZNntjoKiwzceLvG10FoHnqAa5LOZMnT2p0FZaZOLF5/v95ceEDja5C1/KC4NYdrm6m\nL5hJzfEF0yz1ANelnMlXN8+/2UlN8pkAy61lavlIOkbSzGxT97skbd1B/p0lTZW0QNLjkg6r5noO\nimZmVrtqG4k5GoqSRgFnk/awHU7amGFKpa3+JK0L/IW0C9IWwC+ASyTtnvc2HBTNzKxZjQEuiogJ\nETEDOBqYR9GiLSW+BjwVESdExGMRcT5wTVZOLg6KZmZWMyGkKl4dNBUlDQC24t29b4mIAG4Ctqtw\n2iey48WmtJN/OR592nVWAnjssUr7IOczZ85c7rvvvpors8IKtf/+M2fOHKZNm1ZzOb2lHtA767J4\n8dKa61GXf7MD6vBvdu4cpt1X+2cyZ8kLNZexJBbUVM7bS18t/HGlmivTBWbMmN5xpuryDwH6A7NK\n0mcBG1U4Z1iF/KtJWjEiFnZ0UaXAa/Um6WDgykbXw8x6nUMi4qpGV6JA0odJ2+0N6sTpC4GPZutY\nl5b7AdI+tttFxN1F6WcCO0bEcq0/SY8Bl0XEmUVpe5OeMw7KExTdUuw6U4BDSLt3NPeW2WbWE6wE\nrEv6bmkaEfGspE1ILbtqzS4XEAvHgKXA0JL0ocDLFc55uUL+uXkCIjgodpmIeA1omt/mzKxXuKPR\nFSgnC2yVgltny1wsaSqwK2lrPyQpe//LCqfdCexdkrZHlp6LB9qYmVmzOgc4UtKhkjYGLiR1014O\nIOkMScWbQFwIrC/pTEkbSfo6MDIrJxe3FM3MrClFxORsTuKppG7Q+0kbwBdGHQ0D1i7K/7SkfYCf\nA98CngeOiIjSEakVeaCNmZlZxt2nZmZmGQdFMzOzjINiDyXpw5I2b3Q9mpmkhv77lrSBpC0aWQcz\nq46DYg8kaXVgGrBho+sCIOl9klZrdD2KSfoYcJik/g26/uqk+WQfa8T1y8mGszcdSas2ug7FGvFv\nRtIBkj7U3de15Tko9kz9SIvi1raGXB1k/yM/AHy00XUpyL7Uzgc2ioja1izrvLeAAcBLjQ5GklaV\nNCAiotF1KSXp28DXJA1rgrp8TtLqEbG0OwOjpH2BK4B3uuuaVpmDYs+0CmnjlXmNrgjwYWAx8FCj\nK1KQBcJVSCtidLus23ZNUlB8LRo4xFvSR4HrgYOztR+bJjBKOou0JdB/Sct9NbIuRwF/AK6StGY3\nB8YBpOXM3u6m61k7HBR7CEnDJK2VvV2dtKTSgAZWqWA1oBVY0uiKFGRBaTHpi6Y7r7uWpMER0Ur6\nu1mJ9Nk0RBb8jgK2Bw4GPitpYDMERkkHAAcCe0XEn4B+koZIWq9BVVoK3Jf9vLIQGLvp2gOBNyJi\ncTddz9rhoNgDSBoMXAJcKOn9wKvAfLKWoqQVCoNKumNwiaTVJBUW/x1I+vJfuZEDWyStLemLklbI\ngtIapMDYLc/SJA0E/gFcK+k9pL+bBTQwKGYt1LuASVldfgj8T9GxRloPmBYR90jan9RKuwv4p6Qf\nNaA+LwJvAJOB9wK/g/RvR9IH630xSbsVPUtdG1i10QPDLPFfQg8QEXOAW0itsnHAjsAjwMBsz7H3\nkP6nWoEUnDbpqrpkz36mAF/Ogs1SUgCYB0RpgO6mgNQPOAX4HnBIds1le3t3RwCIiEXAl4ANSLuj\nfJT0mQyR9F5JQyWtk7X415S0jaQ1u7pepKA8iP/f3pnHW1lVffz7AxUBc84RcUBLZRBxwAFTytI0\nraxMxUgTFefX2TRfZ000yyEHCunVLDPNqajMAqdIUFQUlVdUcMC3a4oTDoSs94+1jvfhcC8Q9zzn\nXu5dv89nf8559t7P2evs/Tx77bX2WmvD1/DYlCdL2l3S9ZL2qUP786Ew8fcC3owF33XA74AzgIuA\n0+MkhHrRJOBN4CMzuwH4CdBF0jh8n2/HWqpSJQ0CrgJ+GFmzgTkUzqIvttfaUn1HQ0a0acOQtDKw\nipm9GNfD8ZX+GkBffJJbCWdMFXQG3gK2M7Pqc8VqRddd+KQ2Alfj7mlmn2+m7upmVvreXqiWfwSs\njzRsc5YAABnUSURBVK/2D8Mnt+n4ZDMXD2vYCZcipxWPo2lBu6sBnc2sIa77A3/BDW3WpvE8uOWB\nFfB9o3lBU98Sx6iTmc0LpnOrmX0x8m8HdsLVu7ua2URJqrfkKOkbeCiu2/HxONjM5kbZt4Drg74W\nj9Fi0lOR9A+IUx+Ow5nW28BWZvaqpM61UKlK6gqcBuwG3I8/m2sClwDv4M9pV1wb9DFuMPa3lrab\nWDxk7NM2Cnnw2xHAC5KuNbNnzezaWDQeglueXgM8hU9wwl+k2cALtZ5s40XuYmZvmdnekn4JHINP\n+LtImogbt7yHP1ddgqaZkvYxs3dqSU/QtBbu8vC0mc2MiewaYCiwKXA1vnBYBe+jj/AVeSdgUA3a\n3yzamyrpHDObaWaPS9oVuBnvi2Px42wsaJiDT3QvlzBGPYANzeyBYIidot31JW1lZo8GTd2AGUAP\nSZMX90idGtBXZCqTgIn4Im9ihSEGnsQlt1L2zIPpzqqKh7kszpxXl/QermqeiD/DIyUdVIi3uaTt\n9sWPMJoh6UJ8cbQTsC2+YNoVj+X57yjrhL9HN+AMO1EHJFNsg4iX517gDuB2M/vE9SIYY2c88vsA\n4A4zeznuK2XFHxaMpwHjJf3ezF4zswMljcYZ0HhgHL7KrTCdrvgE/JeSGGJvfLJ4ArcYbDCzhpCm\nL49qf8JX37Nx6Xo2vrfW1czebGH7ffH/fAP+H2dWyszsCUnfxiXGvYDDzKxUy0L5Qa+PAY9JGmFm\n98Te6juS/g68L+kaYDCwM3A6LqkZ/pyVSdsOwPiKRaeZfWxmL0q6GdgS2FPSHmY2Jm6Zje/vlfEs\nX4K/O6fKjaLeBjCz2SFF7wkciR9KezQ+fmcD38W3Lpa03dNwpjdO0lVm9laoiD/GtS1vAyfg/717\nXHcDljOzR5a03cQSwMwytaEE9ABewFU3narKVPh+BPAg7t+0QYn09MOtOG8E9o68ToXy0fj+5hBg\n+Tr1UR9ckvgR0K+6f3BV1G3AA8D3CuWdatT+OsDTwPlNlBXHaABuFHUHsHrJffJVXLr4X/wcz90K\nZddE2WvANpG3DC7NblQyXf+Nq5LPL4zPsoXybwAPRz9dCByPaz9+WwItR+Gaje2bKT8r+mkkfko7\n+AJvuxa2OyLeoQMr/V15FnG1+jn4wnIEsEIzv1GTZzfTYoxXaxOQqWpAYD/gPuBThbzPRv5I4MRC\n/nBc1XQdsEwJtGyIH71yUfXvVzHGm3F17kHAiiX3z6qxGLi4ibIu+B4s+H7erbiB0tE1pmFX/LDX\ndfD9xAqjPiCY8SnAgMjvj+8Z3VT2xAaMwqXTicDdwJcifwf8/LkKTZ3r9CwPBZ4HxsSYndcMY9wW\nOBOYFnRfWShTDegQvrf7S+CkyPtC9Mmf8YXdmpG/Z4UxVbe9JLQABwMvE4uRqrKu8bk8vnj4Oy69\nd6/H+GRqZsxam4BMVQMChwaD2Tiuh8ak8ny8NHOBXxbqH0xJkiKuMr0bV+FU8tbG90GGAXsU8m/E\nV+FDajGRLYSmTXBJYqdC3vbBiB7HV9wViXYt4J7ov5VqSMNhwNuF66HBjKYCE3Bp7U5g3SjvC3ym\nxD5ZLj6H44esDsTdG8YAO0dZ1zo/x51xbcfVuEXuD4OmJhljXHdjfkm7ZosIXOIbh0vUA3FNw1XB\nhJ6JMdukhHavBS4vXAtfVF0e43NU5HfBJdVpwJB6jlWmqjFrbQIyLSB17YhH+PgDvif2LnAxofIB\n9sD3IQbVga4rcdVfZRLbF/gtHinmVdw67tRC/WuBXiXRUpn4t8YtSiuM7/CYbMfiFou/wRcOO0T5\npyvMqYXtbwGcEN/XwBcuL+CSxvvABYSaDV8YvEEL1W6LoGdtCguDyFstxmU/3Ap3QjxHXyjUKW3B\n0gSNKxLqbdz3b0QTjLHIBJcpg87K7wJ/BX6NL/bOKpR3xvdj/1BCH9yMawkqUuFonDlPibJ5lXcI\n34f/Wr3GJ1MzY9baBHT0hK+iz8cNNobHinFvGn23dqKgTgG+iO9nlcJ8qmg7DrfYPAvfu3w9GOXn\nceOAHwYtZe9LDQiG+6m4vjuYznMVxgz0ibK1cKn6pBq2vwVuoHNBXHcKmq7AVdpb45a5lfr9ol+2\nLqk/NsTdbt7AFy3bE9oCXIq9Nb5viatSb6cg1Zc8VocAK8f3CuOr7J9VGOPDwLmR1wu4pGxa4vpL\n+HbDP4GTI69LfO6DS2lrtZQhVz0Lh+GWvvdFuxPwbYbKs3xxlK9e9Rt1W7xkmj+l9WkrQn6s0L34\nKrUnvp/xOWC4md1V8TWrum0wLqnNKoGenvH76wG/M7PLww/vK1HlIOBhC79DSTPxlW6LTNUXQdMW\n+CR6uZm9C2Bme0k6GDcWGWtm06pu+xfuilGr9scDl5nZGdH+PNylYFIzvmsH4Ja302tBQxPYFF+s\nTAU2Ak4G1pV0KdAAbCxpezMbL2kYLt0PlTTOzEqLlyuPH3otcJKkHcxsljzC0Nx4lt+SdBFuVbqr\npNWB3SkhRm1TtOD99QDwPXwPGJvfHaUB+NCCKy1hu8OBz8V7c5uZjZQ0G7cLuBf3nX2/8Mx8hKv9\n57OGbgkNiRaitblyR01Ab1ztdgaxt4IbG7xDqFCYX53UE19lv03B4rKG9GyBM5Kn8Bf1TeDQKFuR\nwuq3cM9luBTSpMVcjWiaDVz4H9xzHq7aXK8G7W8e/X1RVf7uuOM9zK/+2wBf+c8qY4yqaNgXn+Cv\nwK0aD4r/PRJfqNxFoxTUG/dfLPuZ3gffm3s+nqXVIr+ivqxIjJ/CwxbOA35TuL+WKtNqWlYvjOmo\naHsULk3vADwCXNPCNi/DF4jXAZNxJnsBzRjB4VbSE4Fzyh6bTP/BOLY2AR0x4Xs/z8WLWDRiWQG3\nVDuqqv7ZuCXlk8AWJdDTN5jPWbj6qDtuKNIArBN1ipP/SrhF6huE2rIEmjYM5nx+XFcm1GOAbzRR\nfwBuNPEG0L8G7Sv6/ANcXVxRBZ6Jq8E2q6p/GO5g/XgZY1Rop3Ph+1Bcih4d47Y2rnq/nzDWoI6m\n/MBmwYi+jy+WXgZWjbLqBd4M4JZCXk3pbIKWVwqMcQPcpWl6POPPADcXx34J2rsIV2lvXMj7dbS7\nYfF3cQvqQcE4725Ju5lqn1qdgI6acMu38bgp9hqR1y8YwV5Vdb+K+1itXwId6+Kr5puq8gcEo9y1\nKv8C3A9uWi2YTzM0CbfCnQlcVcg/HZdgP1dV/3BcnTmOGjJpPBLOWNydYGBMsA1U7c/hatztcYmt\nxRJqE3RUJK7KpFpcSA2J//4/QO96Pb9V9BUNxU7FraQHR79Nr2aM8ez/pan7S6alKL12woPZ9yOs\nTpeUFtz4bR7ww6r8neJ56V/IWwdfbN0HjCqjDzK18BlqbQI6csLVLY/iDsv946W9olBefMFL8y3D\nV6xT8NXr8pE3OJji1lV1j8KjxJRq6IOrbI8I2n4SE1wD8OVm6g8mFhctbLdHMJojcf+x1WJifQVX\npe4e9RZY1TeVVwN6euHWtFfgUmq3JuoMiedoNCUtVJqh7RBcxb16IW8AbvG6VdA+ocgYo06npr7X\niZYZRVpqMX645uRXuIR+LI2+qz+O/75yVf198HjBNe2DTLVJrU5AR0kx2e4bL8SWhfwf4Sq3d4DR\nhfyyHb3F/BLHw/j+y2YxgbwGXNrMvcuVRNMauKHRYFyFu0wwpyn4SvyLUa+oQqzZYgHfe3sc97m8\nmEaV7Ur4Qb1TcevfSn7p6i7cyXwevp98I+4GchzhclKoNxR3d7iVklTaVe0dEnQ9i+/NHVcouwEY\nE983Ax4CXqQkC8ta0NKCtiuLyO74PukEfJFyBq7VGBjlnZp6p+vxDGX6D8e0tQnoCAnfs5uOb6r/\nH24E8dlC+QUx2Z1Fozl7aUwRP9boStzl4/uF/Im4RPQaBaODshl0oY+eDcY8L5jQNnig5qNxifHa\nQv2aSs7BEN/EDXVWLOR/Hfcd7YarZ8fj6rJ6MsYraPRlOxV3iG/A97G+Uqg3BFfLrVMyPcLdG6YE\nHcPw/cM7cRX3lrjaeavC2D6HW/C2C1qA/fEF7cN4ZJz9gvGNimf4fUKrQQnRpjKVl1qdgPaecCfq\nSqi07sCXg+lsW1Xvx7jhzQ+IfY+S6NkiJo/bcUOAOVWM8R4ao/fXZRWL7+vMxv0et8Ql6rdw52bh\nqtRjcCnu54X7asIYccOH+yiEF4v8U6Mv7gO2i/Ebi1t9fq2O/XMa8FBV3qQYx0m4gc/XcMm6LiHC\ncIf3nePZ/gWuZh4Wz8+s6LejCvV7tBda8O2D6fF8/hzXIMwNhrg68FPcT/UwqixvM7X91OoEtPcU\nL8ZY5rfe/EPkDyVUgpF/SawyTynjJQrm8z7zO6FfGQy5KB2NxVVMO9SK8SyEpo3xqD0jq/LPiAlt\n/bheIRjjIxTM+GtEw2a44dBgGiXA4fiC4ciYXP+MG9N0w6XWP5bBgHDDp2/jksfWhfypwCnx/Rf4\n3tjOuAHQg0HTWnV4nufbDwR2wS1+byrkD8XVzwvQQ23dLupOC36SxWt4wIYKw1sv8j/EoyopmOXf\n4/lZ9j9tJ1PrpVYnoL0n3DLyeWIfMSb7eXiszAm4ef+wQv3zKMGnLF7c1ymYwUf+zXjwgGdw5+K9\nIn8chT2REvtndxr3zIpWgIfgKuWNaLS6XAFfMNwPrF1DGg7EV/rFhUsPIowa7uh9Ly6VfRqXLDco\noS/6xbMyBWfIT9PoWvFfMdH+vjIpV9376ZLHaSBNhMsLBrALLrUW3QsqDKMM46O60xK/3R1fIB1b\nyKs8myvFGM2J56k7HoD8OQph9jK1/dTqBLT3hPvbPRQvx63BAL4aL9QaeGDgsUSU/hLp2CCY8J3A\njpF3Gq62/EEwoWdwtVDPKL+Xgt9Vjen5NL7aXhvfE3oFl1pXjrJ/AecV6hcZ4yo1pmUQvsrfp9hW\nfK9IjodG/5WiBqRRhXwxbra/Bx6rcxLu5L0FrlKeRYEhU4cTL3BpZx6+VzeMBVX/ywQz+id+vud8\nY9ZeaMGl+Ldo3CusPkVjnRivX8f1Knh0qlLHJ1ONn7HWJqAjpGCM++JO+L+tKjsV3ysr/SxC/ISJ\nPwZj/FlMHF8qlPeMCaemRy01QcfmuMrvHjycHMB3YqIbzYL+iQsEj64xPT2iL+6kGV9Q/IDZWygc\n6VXD9puT4g8NRrl5XJ+E72fWTEpeTPoOxv0gv4PHnX0IV+H2pjHQdSdcndsAPNAeacEj8TQApzdR\nVnlGz8OjQnVtqjxT20+dSJQOM3vRzG7BpaGukpYrFK+JS2ed60DHc7g5f1fcUnGEmd0jx7L46RuT\ncQtZJKnWNEjqjU9k9+Er/X2DthtxyXVvXG17ZYFuK37WGmb2Ci6B7A6cJ2nzAr0rShqBx8s8xyL+\nao3RGd/D7SJpUCF/Or4HvGxcT8YNOfqUQMPCcD9uIPZP/FDg43EV8kjgFkkD8T3p+3BmNbmd0mL4\nXu6eknpVMqvek1WA8Wb2gaRPYkuX9ewmSkBrc+WOlHAJ6S18xX8gjXEy+9aZjl644cgY5j+X8Fx8\nH6/mUVni91fFJZ3Lq/KLIcD2xRcPP6Ek1W0ztHXG93//jauRR+EBpe/G9/C2LLn9ihT/Z9zwZwVc\nKrm4qt6DwIN16I9OVZ/H4C48PeL6M9FXU/Djqn5PGAIVfqNWfohtiZbB0dYvqDodBt8OeSbe8ceB\nE6nzOZaZajDGrU1AR0vxUk3D4zKOpeTA0QuhozIJ/wl3gzgFj/NZ2uQfi4JpuIN+p6qyotHCEHxF\nfn31xFOHfhkI3BaT2gO4K01dmHOMyRgajZx+XCirnCe5Yz3ooWqvEo//+hS+b7YKLqmNjrKvxALi\nxvZOS7RxJG5Q87dg0H2AbwJPxDu9H/AtSrYTyFROqkxCiTpC0qq4SuwjM3urFenYBA81ty0+uWxv\nZo+W2N4B+H7QcmZmTR2NJalb0LI1zpAGm9k/y6KpGTqbOg6qXm1vQhzWDAw1s/sjv6ljxMpof3+8\n7wfhAegnmdnVUTYaXzSsgbsVHWlms6Osi8UxTJJkNZhY2hItVXRVAgb8BN+P7oq7Cj1uZsNr2Vai\n/kim2MEh6bO4O8TpZjal5LZ2wC0qDzSz25qpcwxuBTpY0opm9k6ZNDVDwycTaRmT6mK0vzG+pyrc\nAvehOrV7CS7h/AM/D3InPPjEPXhwgD64K9EYQtVc3Tc1ZIhthpaF0LgK7re6BvCqmTVEfqstqhIt\nRx4y3MFhZlMlfdPM/l2H5mbgMV6HSnrEzGbAApPXBsDkMFIow6hlkShOpPVmiNHmNEnH4lL8pZKO\nN7N/lNmmpBPwfe69cIlnrqT1cMZ0Lu5m8G1Jj+PnZ86J+1Tr/mpLtCwM5gcXz8L3MSu0Kxni0o20\nPk1QJ4aImb2Kn3yxGwUrz1CldpN0IW5ReLWZzW0NhtRWYG4pfDJudDSzrHbC8rg7bnl7kZk9Anwc\nk/vLuMHRD4Cvh/r7B8AukvYOOms2Rm2JliVFW6Ah0TIkU0zUG3fgbiH7A7dJul7S1Xgc1kOAr5vZ\n1NYksK3AzJ7FI9q8VGIbhgdM2BYPMFHMx8zexv0zn8IDCjyLS/A92jMtiY6LZIqJusLM5pnZdbgV\n5VO45Wsf3JR9kJk91pr0tTVUVIMl4x3cmnLLaPMTaSektJm4MUt/cz/NgyoGL+2clkQHRO4pJloF\nZjZB0n65/9ImUHRK/42ZPQ9NOqU/HN/HR3kZFrFtiZZEB0RKionWxCeTWBnRcxKLBzN7D/dT3RY4\nU9JGkW+x37sGftjxN8K45VhJXctgQm2JlkTHRLpkJBIJACQdifvePYiftzkW2BQ4Ew8mcB0eCvB+\nK9l3tC3RkuhYSKaYSCSAtuWU3pZoSXQsJFNMJBLzoS05pbclWhIdA8kUE4nEItEakX2aQ1uiJdH+\nkEwxkUgkEolAWp8mEolEIhFIpphIJBKJRCCZYiKRSCQSgWSKiUQikUgEkikmEolEIhFIpphIJBKJ\nRCCZYiKRSCQSgWSKiUQikUgEkikmEouApPUlzZPUL653lvSxpBVbgZaxki5rwf0vSjq2ljQlEu0J\nyRQTSyUkjQ5G9bGkjyQ9J+lMSWU908XQTw8Ba5vZO4tzY0sZWSKRqB/ykOHE0ow/AgcBywNfBq4G\nPgJGVFcMZmktiJn5yXmPZjYXaFjC30kkEm0YKSkmlmZ8ZGavm9nLZjYSuBf4KoCkgyTNkrSXpCnA\nh8B6UTZM0tOSPojPI4o/KmlbSZOifAKwJQVJMdSn84rqU0k7hkQ4W9Kbkv4oaSVJo4GdgeMKkm3P\nuKePpDGS3pX0f5JukLRa4Te7Rd67kl6VdMLidEr85wlB/+uSbltI3eMlTZb0nqSXJP1UUvdCeU9J\nd8V/ek/Sk5J2j7KVJd0kqUHS+5KmSvru4tCYSLRVJFNMtCd8CCwX3w0/cugU4BCgN9AgaQhwNvB9\n/NDa04FzJX0HIBjC3cBTwICoe2kTbRWZZH+cIT8FbAdsD9wJdAaOA8YDPwPWBNYGXpa0EvBX4NFo\nZzf8eKRbCm1cCuwE7IWfLbhL1G0WkvYEfgf8Hugf9/xjIbd8DBwDbA4MBQYDFxfKr8b7dBDQBzgV\neC/Kzsf7cLf4PAL418LoSyTaOlJ9mmgXkLQrPjlfXsheBjjCzJ4q1DsbONHM7oysGZJ6A4cDNwJD\ncFXpMDObAzwjaT2cOTSHk4GJZnZMIW9qoc05wPtm9noh72hgkpmdWcgbBrwkaWPgNeB7wAFmNi7K\nvwu8soiuOB34lZmdW8ib0lxlM7uicPmSpDOBa4CjI2894FYzezqupxfqrwc8ZmaPVe5fBG2JRJtH\nMsXE0oy9JL0LLIszspuAcwrlc6oYYjegFzBK0s8L9ZYBZsX3TYHJwRArGL8IOvozv4S3ONgC+HzQ\nX4QFjd3w/zXhkwKzWZKmsnD0B0YuLhGxmDgN/98r4n3RRdLyZvYhcAVwjaTdcGn4NjN7Mm6/BrhN\n0lbAPcAdZraovkok2jRSfZpYmvE3oB+wMdDVzL5nZh8Uyj+oqr9CfA7DmVIl9cZVnkuK6nYWBysA\nd+H0F2nZBLi/HrRIWh9XFT8O7IOrZo+K4uUAzGwUsCFwA64+nSjpqCj7E9ATuAxXC98raQEjp0Ri\naUIyxcTSjNlm9qKZvWJm8xZV2cwagJlALzN7oSrNiGrPAP0kLVe4dVEMczLwhYWUz8H3F4uYhDPj\nGU3Q8gHwPDAXGFi5QdIqwGdaSEsRW+EHjZ9kZhPMbBqwbnUlM3vVzEaa2TdxBnhooewNM7vRzIYC\nxwOHLWbbiUSbRDLFREfDWcD3JR0jaZOwAD1I0vFR/itchflzSZtJ2gM4sYnfUeH7RcA2YbnZV9Km\nkoZLWjXKpwMDIwhAxbr0p8CqwM2Stpa0kaTdJF0vSWY2GxgFXCJpsKQ+wGjcMGZhOAfYX9LZQUdf\nSac0U3casKykYyVtGMZGh8/3J6UfS/qSpA0kDcANcZ6OsnMk7S2pV+zLfqVSlkgsrUimmOhQCHXg\nMOBgXKoaB3wXeCHKZ+PWnn1wae483IJ1gZ8q/OZzuHVoP+Bh3Ll/b1zSA7ci/RhnGA2SeprZa8CO\n+Dv456DlMmBWwZfyZOABXM16T3x/dBH/7z7gW/EfHsP3Abdphu7JwAnx/54E9sf3F4voDFwVtI8B\nnqVRxToHuBB4Au/HufEbicRSCy25L3MikUgkEu0LKSkmEolEIhFIpphIJBKJRCCZYiKRSCQSgWSK\niUQikUgEkikmEolEIhFIpphIJBKJRCCZYiKRSCQSgWSKiUQikUgEkikmEolEIhFIpphIJBKJRCCZ\nYiKRSCQSgf8Hbcz9fszDMgAAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x683e1a6cf8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Normalize the matrix\n",
    "\n",
    "conf_matrix = conf_matrix.astype('float') / conf_matrix.sum(axis=1)[:, np.newaxis]\n",
    "conf_matrix = conf_matrix.round(decimals = 2)\n",
    "\n",
    "df = pd.DataFrame(data = conf_matrix, columns = classes, index = classes) \n",
    "print(\"Confusion Matrix\")\n",
    "print(df)\n",
    "\n",
    "fig1 = plt.figure(figsize=(8, 4), dpi=100)\n",
    "plt.imshow(conf_matrix, interpolation = 'nearest', cmap = plt.cm.Purples)\n",
    "ticks = np.arange(len(classes))\n",
    "plt.title(\"Confusion Matrix\")\n",
    "plt.xticks(ticks, classes, rotation=45)\n",
    "plt.yticks(ticks, classes)\n",
    "\n",
    "plt.ylabel('True class')\n",
    "plt.xlabel('Predicted class')\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.colorbar()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['decision_tree2.1.pkl']"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.externals import joblib\n",
    "\n",
    "joblib.dump(grid_search_cv, \"decision_tree2.1.pkl\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pickle\n",
    "from sklearn.externals import joblib\n",
    "grid_search_cv = joblib.load(\"decision_tree2.1.pkl\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
